title: MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model.
abstract: BACKGROUND
  To understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs), microRNAs (miRNAs) and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date.
  
  
  RESULTS
  To identify and analyze molecular interaction networks, we developed MIR@NT@N, an integrative approach based on a meta-regulation network model and a large-scale database. MIR@NT@N uses a graph-based approach to predict novel molecular actors across multiple regulatory processes (i.e. TFs acting on protein-coding or miRNA genes, or miRNAs acting on messenger RNAs). Exploiting these predictions, the user can generate networks and further analyze them to identify sub-networks, including motifs such as feedback and feedforward loops (FBL and FFL). In addition, networks can be built from lists of molecular actors with an a priori role in a given biological process to predict novel and unanticipated interactions. Analyses can be contextualized and filtered by integrating additional information such as microarray expression data. All results, including generated graphs, can be visualized, saved and exported into various formats. MIR@NT@N performances have been evaluated using published data and then applied to the regulatory program underlying epithelium to mesenchyme transition (EMT), an evolutionary-conserved process which is implicated in embryonic development and disease.
  
  
  CONCLUSIONS
  MIR@NT@N is an effective computational approach to identify novel molecular regulations and to predict gene regulatory networks and sub-networks including conserved motifs within a given biological context. Taking advantage of the M@IA environment, MIR@NT@N is a user-friendly web resource freely available at http://mironton.uni.lu which will be updated on a regular basis.
authors.affiliation: Cytoskeleton and Cell Plasticity Lab, Life Sciences Research Unit-FSCT, University of Luxembourg, L-1511 Luxembourg, Luxembourg.
authors.forename: Antony
authors.initials: A
authors.lastname: Le Béchec
authors.forename: Elodie
authors.initials: E
authors.lastname: Portales-Casamar
authors.forename: Guillaume
authors.initials: G
authors.lastname: Vetter
authors.forename: Michèle
authors.initials: M
authors.lastname: Moes
authors.forename: Pierre-Joachim
authors.initials: PJ
authors.lastname: Zindy
authors.forename: Anne
authors.initials: A
authors.lastname: Saumet
authors.forename: David
authors.initials: D
authors.lastname: Arenillas
authors.forename: Charles
authors.initials: C
authors.lastname: Theillet
authors.forename: Wyeth W
authors.initials: WW
authors.lastname: Wasserman
authors.forename: Charles-Henri
authors.initials: CH
authors.lastname: Lecellier
authors.forename: Evelyne
authors.initials: E
authors.lastname: Friederich
mesh_terms: D020816:Amino Acid Motifs / Q000235:genetics; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D020407:Internet; D035683:MicroRNAs / Q000235:genetics* / Q000378:metabolism; D012333:RNA, Messenger / Q000235:genetics; D014157:Transcription Factors / Q000235:genetics* / Q000378:metabolism
chemical_list: D035683:MicroRNAs; D012333:RNA, Messenger; D014157:Transcription Factors